Time Shifting and Agile Time Boxes in Course Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="3">The ongoing integration of Information and Communication Technologies (ICTs) into higher education courses is often called <em>blended learning</em> although it often relates to course design. It is usually understood in place categories, as a combination of traditional classroom-based sessions and Internet-enabled distance or online learning practices. One alternative understanding of ICT integration can be constructed of time categories, with an understanding of ICTs more as process- and project-related. Two such design frameworks are conceptually presented and then used together in a small case study in a pilot experiment in physics at the preparatory level for entering engineering programs at a university in Northern Sweden. These are a) time shift mechanisms between synchronous and asynchronous learning modes in the course process and b) agile frameworks mechanisms adapted from work process developments in the software industry. Both are here used to address common procrastination problems in flexible education. Data were collected in student interviews and analysed with qualitative content analysis. Results show student satisfaction with the work rhythm and that a feeling of presence, which enables easy interaction, can be facilitated by synchronicity.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it